Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914645240971264 |
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| author | Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Cao, Quoc Hung Truong, Van Binh Nguyen, Quoc Khanh Cao, Hung |
| author_facet | Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Cao, Quoc Hung Truong, Van Binh Nguyen, Quoc Khanh Cao, Hung |
| contents | The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using Explainable AI (XAI) for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09852 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing the Fairness and Performance of Edge Cameras with Explainable AI Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Cao, Quoc Hung Truong, Van Binh Nguyen, Quoc Khanh Cao, Hung Computer Vision and Pattern Recognition Artificial Intelligence The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using Explainable AI (XAI) for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models. |
| title | Enhancing the Fairness and Performance of Edge Cameras with Explainable AI |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.09852 |